[Feature Improvement] Add support for Ollama, Gemini, and Claude, with Web UI

.gitignore

@@ -13,4 +13,9 @@ research_dir/*
 state_saves/*
 __pycache__/*
 Figure*.png
-testrun.py
+testrun.py
+/data/
+/.vscode
+/settings/user_settings.json
+/AgentLaboratoryWebUI
+settings/task_note_llm_config.json

README.md

@@ -33,95 +33,229 @@
 
 ### 👾 Currently supported models
 
-* **OpenAI**: o1, o1-preview, o1-mini, gpt-4o
+* **OpenAI**: 
+  * GPT-5 series: gpt-5.2, gpt-5.2-pro, gpt-5-mini
+  * GPT-4.1 series: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano
+  * GPT-4o series: gpt-4o, gpt-4o-mini
+  * o-series (reasoning): o1, o1-preview, o1-mini, o3-mini, o4-mini
 * **DeepSeek**: deepseek-chat (deepseek-v3)
+* **Anthropic**: 
+  * Claude 4.5: claude-4.5-opus, claude-4.5-sonnet, claude-4.5-haiku
+  * Claude 4.1: claude-4.1-opus
+  * Claude 4: claude-4-opus, claude-4-sonnet
+  * Claude 3.x: claude-3-5-sonnet, claude-3-5-haiku, claude-3-7-sonnet
+* **Google**: 
+  * Gemini 3.0: gemini-3.0-pro, gemini-3.0-flash
+  * Gemini 2.5: gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite
+  * Gemini 2.0: gemini-2.0-flash
+* **Ollama**: Any model that you can find in the [Ollama Website](https://ollama.com/search)
 
 To select a specific llm set the flag `--llm-backend="llm_model"` for example `--llm-backend="gpt-4o"` or `--llm-backend="deepseek-chat"`. Please feel free to add a PR supporting new models according to your need!
 
 ## 🖥️ Installation
 
-### Python venv option
-
-* We recommend using python 3.12
+> [!IMPORTANT]
+> We recommend using python 3.12
 
 1. **Clone the GitHub Repository**: Begin by cloning the repository using the command:
-```bash
-git clone git@github.com:SamuelSchmidgall/AgentLaboratory.git
-```
+    ```bash
+    git clone git@github.com:SamuelSchmidgall/AgentLaboratory.git
+    ```
 
 2. **Set up and Activate Python Environment**
-```bash
-python -m venv venv_agent_lab
-```
-- Now activate this environment:
-```bash
-source venv_agent_lab/bin/activate
-```
+
+    Python venv option
+    ```bash
+    python -m venv venv_agent_lab
+    source venv_agent_lab/bin/activate
+    ```
+
+    Conda option
+    ```bash
+    conda create -n agent_lab python=3.12
+    conda activate agent_lab
+    ```
 
 3. **Install required libraries**
-```bash
-pip install -r requirements.txt
-```
-
-4. **Install pdflatex [OPTIONAL]**
-```bash
-sudo apt install pdflatex
-```
-- This enables latex source to be compiled by the agents.
-- **[IMPORTANT]** If this step cannot be run due to not having sudo access, pdf compiling can be turned off via running Agent Laboratory via setting the `--compile-latex` flag to false: `--compile-latex "false"`
-
-
-
-5. **Now run Agent Laboratory!**
-
-`python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA"`
-
-or, if you don't have pdflatex installed
-
-`python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA" --compile-latex "false"`
-
-### Co-Pilot mode
-
-To run Agent Laboratory in copilot mode, simply set the copilot-mode flag to `"true"`
-
-`python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA" --copilot-mode "true"`
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4. **Install Higher Version of Gradio**
+    ```bash
+    pip install gradio==4.44.1
+    ```
+
+> [!NOTE]
+> This is only required for the current version of web interface. 
+> We will move to `Flask` in the future for capability of the package.
+
+5. **Install pdflatex [OPTIONAL]**
+
+    ## Instructions for Ubuntu:
+    ```bash
+    sudo apt install pdflatex
+    ```
+
+    If you find the package is not available, 
+    you can install it via the following commands:
+    ```bash
+    sudo apt-get install texlive-latex-base
+
+    sudo apt-get install texlive-fonts-recommended
+    sudo apt-get install texlive-fonts-extra
+
+    sudo apt-get install texlive-latex-extra
+    sudo apt-get install texlive-science
+    ```
+
+    ## Instructions for Windows Users
+
+    To have a TeX engine on Windows, you need to install the MikTeX software. Follow these steps:
+
+    1. Go to the [MikTeX download page](https://miktex.org/download).
+    2. Download the installer for your version of Windows.
+    3. Run the installer and follow the on-screen instructions to complete the installation.
+    4. Once installed, you can verify the installation by opening a command prompt and typing `latex --version`.
+
+    - This enables latex source to be compiled by the agents.
+> [!IMPORTANT] 
+> If this step cannot be run due to not having sudo access, 
+pdf compiling can be turned off via running Agent Laboratory 
+via setting the `--compile-latex` flag to false: `--compile-latex "false"`.
+Or you can disable by unchecked the `Compile LaTeX` option in the web interface.
+
+## 🚀 Quick Start
+
+1. **Set up the configuration file**
+
+   - You can set up the configuration file by editing the `config.py` file.
+   - See the [configuration file](./config.py) for more details.
+
+2. **Now run Agent Laboratory!**
+
+    #### Basic Usage of Agent Laboratory in Web Interface
+    ```bash
+    python config_gradio.py
+    ```
+
+    #### Basic Usage of Agent Laboratory in CLI
+
+    ##### 1. A simple command to run Agent Laboratory
+    ```bash
+    python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA"
+    ```
+
+    ##### 2. Available Configuration Options
+
+    **API Keys:**
+    - `--api-key`: OpenAI API key or set to "ollama" for Ollama usage **(required)**
+    - `--deepseek-api-key`: DeepSeek API key 
+    - `--google-api-key`: Google API key
+    - `--anthropic-api-key`: Anthropic API key
+
+    **LLM Settings:**
+    - `--llm-backend`: Backend LLM to use (default: "o1-mini"), please ensure your model string is correct, here is some common models:
+      - OpenAI: "o1", "o1-preview", "o1-mini", "gpt-4o"
+      - DeepSeek: "deepseek-chat" (deepseek-v3)
+      - Anthropic: "claude-3-5-sonnet-latest", "claude-3-5-haiku-latest"
+      - Google: "gemini-2.0-flash", "gemini-2.0-flash"
+      - Ollama: Any model that you can find in the [Ollama Website](https://ollama.com/search)
+    - `--ollama-max-tokens`: Max tokens for OLLAMA (default: 2048), 
+
+    **Research Parameters:**
+    - `--research-topic`: Your research topic/idea or a open-ended question to ask, this **must be provided**
+    - `--language`: Operating language (default: "English") which will instruct the agents to perform research in your preferred language (Not fully supported yet)
+    - `--num-papers-lit-review`: Number of papers for literature review (default: 5)
+    - `--mlesolver-max-steps`: Steps for MLE solver (default: 3)
+    - `--papersolver-max-steps`: Steps for paper solver (default: 5)
+
+    **Operation Modes:**
+    - `--copilot-mode`: Enable human interaction mode (default: "false"), you need check terminal for input in this mode
+    - `--compile-latex`: Enable LaTeX PDF compilation (default: "true"), **please ensure you have pdflatex installed**
+
+    **State Management:**
+    - `--load-existing`: Load from existing state (default: "false")
+    - `--load-existing-path`: Path to load state from (e.g., "state_saves/results_interpretation.pkl")
+
+    <details>
+    <summary>📚 Example Usage</summary>
+
+    Basic run without PDF compilation:
+    ```bash
+    python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA" --compile-latex "false"
+    ```
+
+    Run in copilot mode:
+    ```bash 
+    python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA" --copilot-mode "true"
+    ```
+
+    Run with custom solver steps and language:
+    ```bash
+    python ai_lab_repo.py --api-key "API_KEY_HERE" --llm-backend "o1-mini" --research-topic "YOUR RESEARCH IDEA" --mlesolver-max-steps "5" --papersolver-max-steps "7" --language "Spanish"
+    ```
+
+    Load from the existing state:
+    ```bash
+    python ai_lab_repo.py --api-key "API_KEY_HERE" --load-existing "true" --research-topic "YOUR RESEARCH IDEA" --load-existing-path "state_saves/results_interpretation.pkl"
+    ```
+    </details>
+
+> [!NOTE]
+> You must at least provide an API key for use. 
+> Even when you run a local Ollama, you must provide an "ollama" string as the API key.
+
+> [!TIP]
+> - Set the `--ollama-max-tokens` to the model real context length (Ex: 128000 for `qwen2.5:32b`) for much better performance.
+> - Use the model that supports `tools` as the Agent Laboratory will instruct the model to output formatted code or actions (This is kinda needed for the current version of Agent Laboratory).
 
 -----
+
 ## Tips for better research outcomes
 
 
 #### [Tip #1] 📝 Make sure to write extensive notes! 📝
 
-**Writing extensive notes is important** for helping your agent understand what you're looking to accomplish in your project, as well as any style preferences. Notes can include any experiments you want the agents to perform, providing API keys, certain plots or figures you want included, or anything you want the agent to know when performing research.
+**Writing extensive notes is important** for helping your agent understand what you're looking to accomplish in your project, 
+as well as any style preferences. Notes can include any experiments you want the agents to perform, providing API keys, certain plots or figures you want included, or anything you want the agent to know when performing research.
 
-This is also your opportunity to let the agent know **what compute resources it has access to**, e.g. GPUs (how many, what type of GPU, how many GBs), CPUs (how many cores, what type of CPUs), storage limitations, and hardware specs.
+This is also your opportunity to let the agent know **what compute resources it has access to**, 
+e.g. GPUs (how many, what type of GPU, how many GBs), CPUs (how many cores, what type of CPUs), storage limitations, and hardware specs.
 
-In order to add notes, you must modify the task_notes_LLM structure inside of `ai_lab_repo.py`. Provided below is an example set of notes used for some of our experiments. 
+In order to add notes, you must modify the TASK_NOTE_LLM structure inside of `config.py`. 
+Provided below is an example set of notes used for some of our experiments. 
 
 
 ```
-task_notes_LLM = [
+TASK_NOTE_LLM = [
     {"phases": ["plan formulation"],
      "note": f"You should come up with a plan for TWO experiments."},
 
-    {"phases": ["plan formulation", "data preparation",  "running experiments"],
+    {"phases": ["plan formulation", "data preparation", "running experiments"],
      "note": "Please use gpt-4o-mini for your experiments."},
 
     {"phases": ["running experiments"],
-     "note": f'Use the following code to inference gpt-4o-mini: \nfrom openai import OpenAI\nos.environ["OPENAI_API_KEY"] = "{api_key}"\nclient = OpenAI()\ncompletion = client.chat.completions.create(\nmodel="gpt-4o-mini-2024-07-18", messages=messages)\nanswer = completion.choices[0].message.content\n'},
+     "note": 'Use the following code to inference gpt-4o-mini: \nfrom openai import OpenAI\nos.environ["OPENAI_API_KEY"] = "{{api_key}}"\nclient = OpenAI()\ncompletion = client.chat.completions.create(\nmodel="gpt-4o-mini-2024-07-18", messages=messages)\nanswer = completion.choices[0].message.content\n'},
 
     {"phases": ["running experiments"],
-     "note": f"You have access to only gpt-4o-mini using the OpenAI API, please use the following key {api_key} but do not use too many inferences. Do not use openai.ChatCompletion.create or any openai==0.28 commands. Instead use the provided inference code."},
+     "note": "You have access to only gpt-4o-mini using the OpenAI API, please use the following key {{api_key}} but do not use too many inferences. Do not use openai.ChatCompletion.create or any openai==0.28 commands. Instead use the provided inference code."},
 
     {"phases": ["running experiments"],
      "note": "I would recommend using a small dataset (approximately only 100 data points) to run experiments in order to save time. Do not use much more than this unless you have to or are running the final tests."},
 
     {"phases": ["data preparation", "running experiments"],
-     "note": "You are running on a MacBook laptop. You can use 'mps' with PyTorch"},
+     "note": "You are running on a Ubuntu System. You can use 'cuda' with PyTorch"},
 
     {"phases": ["data preparation", "running experiments"],
      "note": "Generate figures with very colorful and artistic design."},
-    ]
+
+    {"phases": ["literature review", "plan formulation",
+                "data preparation", "running experiments",
+                "results interpretation", "report writing",
+                "report refinement"],
+     "note": "You should always write in the following language to converse and to write the report {{language}}"}
+]
 ```
 
 --------
@@ -138,9 +272,22 @@ When resources are limited, **optimize by fine-tuning smaller models** on your s
 
 #### [Tip #3] ✅ You can load previous saves from checkpoints ✅
 
-**If you lose progress, internet connection, or if a subtask fails, you can always load from a previous state.** All of your progress is saved by default in the `state_saves` variable, which stores each individual checkpoint. Just pass the following arguments when running `ai_lab_repo.py`
+**If you lose progress, internet connection, or if a subtask fails, you can always load from a previous state.** 
+All of your progress is saved by default in the `state_saves` variable, which stores each individual checkpoint. 
+
+##### **For Web Interface**
+
+You can check out the `Resume Previous Research` section to load from a previous state.
+By checking the `Load Existing Research State` flag and then select the stage you want to load from, you can easily load from a previous state.
+If the state is not up-to-date, you can always click the `Refresh Saved States` button to refresh the saved states.
+
+##### **For CLI**
 
-`python ai_lab_repo.py --api-key "API_KEY_HERE" --research-topic "YOUR RESEARCH IDEA" --llm-backend "o1-mini" --load-existing True --load-existing-path "state_saves/LOAD_PATH"`
+Just pass the following arguments when running `ai_lab_repo.py`
+
+```bash
+python ai_lab_repo.py --api-key "API_KEY_HERE" --research-topic "YOUR RESEARCH IDEA" --llm-backend "o1-mini" --load-existing True --load-existing-path "state_saves/LOAD_PATH"
+```
 
 -----
 
@@ -150,8 +297,11 @@ When resources are limited, **optimize by fine-tuning smaller models** on your s
 
 If you are running Agent Laboratory in a language other than English, no problem, just make sure to provide a language flag to the agents to perform research in your preferred language. Note that we have not extensively studied running Agent Laboratory in other languages, so be sure to report any problems you encounter.
 
-For example, if you are running in Chinese:
+##### **For Web Interface**
+You can select the language in the dropdown menu. If the language you want is not available, you can edit the `config_gradio.py` file to add the language you want.
 
+##### **For CLI**
+If you are running in Chinese, you can run the following command:
 `python ai_lab_repo.py --api-key "API_KEY_HERE" --research-topic "YOUR RESEARCH IDEA (in your language)" --llm-backend "o1-mini" --language "中文"`
 
 ----

agents.py

@@ -1,3 +1,5 @@
+import json
+
 from utils import *
 from tools import *
 from inference import *